GPT-5 Mini vs Claude 4.1 Opus

1. High reasoning performance

• Retains strong reasoning capabilities despite being a smaller, faster model.
• Suitable for tasks requiring accurate logic and structured thinking.

2. Fast and cost-efficient

• Optimized for speed, making it ideal for real-time or high-volume workloads.
• Far cheaper than GPT-5 while maintaining solid capability.

3. Great for well-defined tasks

• Excels when prompts are precise and objectives are clearly specified.
• More predictable and stable for deterministic workflows.

4. Multimodal input

• Accepts text + image as input.
• Outputs text only.

5. Tool support

• Works with Web Search, File Search, Code Interpreter, MCP.
• (Does not support Image Generation as a tool and does not support Computer Use.)

1. Advanced Coding Performance

• Achieves 74.5% on SWE-bench Verified, improving the Claude family's state-of-the-art coding abilities.

• Stronger at:

  • Multi-file code refactoring
  • Large codebase debugging
  • Pinpointing exact corrections without unnecessary edits

• Outperforms Opus 4 and shows gains comparable to jumps seen in past major releases.

2. Improved Agentic & Research Capabilities

• Better at maintaining detail accuracy in long research tasks.
• Enhanced agentic search and step-by-step problem solving.
• Performs reliably across complex multi-turn reasoning tasks.

3. Validated by Real-World Users

• GitHub: Better multi-file refactoring and code adjustments.
• Rakuten Group: High precision debugging with minimal collateral changes.
• Windsurf: One standard deviation improvement on their junior dev benchmark—similar magnitude to Sonnet 3.7 → Sonnet 4.

4. Hybrid-Reasoning Benchmark Improvements

• Improvements across TAU-bench, GPQA Diamond, MMMLU, MMMU, AIME (with extended thinking).
• Stronger robustness in long-context reasoning tasks.